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# Borrow a lot from openai baselines:
# https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py
import gym
from collections import deque
from ding.envs import NoopResetWrapper, MaxAndSkipWrapper, EpisodicLifeWrapper, FireResetWrapper, WarpFrameWrapper, \
ScaledFloatFrameWrapper, \
ClipRewardWrapper, FrameStackWrapper
import numpy as np
from ding.utils.compression_helper import jpeg_data_compressor
import cv2
def wrap_deepmind(env_id, episode_life=True, clip_rewards=True, frame_stack=4, scale=True, warp_frame=True):
"""Configure environment for DeepMind-style Atari. The observation is
channel-first: (c, h, w) instead of (h, w, c).
:param str env_id: the atari environment id.
:param bool episode_life: wrap the episode life wrapper.
:param bool clip_rewards: wrap the reward clipping wrapper.
:param int frame_stack: wrap the frame stacking wrapper.
:param bool scale: wrap the scaling observation wrapper.
:param bool warp_frame: wrap the grayscale + resize observation wrapper.
:return: the wrapped atari environment.
"""
#assert 'NoFrameskip' in env_id
env = gym.make(env_id)
env = NoopResetWrapper(env, noop_max=30)
env = MaxAndSkipWrapper(env, skip=4)
if episode_life:
env = EpisodicLifeWrapper(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetWrapper(env)
if warp_frame:
env = WarpFrameWrapper(env)
if scale:
env = ScaledFloatFrameWrapper(env)
if clip_rewards:
env = ClipRewardWrapper(env)
if frame_stack:
env = FrameStackWrapper(env, frame_stack)
return env
def wrap_deepmind_mr(env_id, episode_life=True, clip_rewards=True, frame_stack=4, scale=True, warp_frame=True):
"""Configure environment for DeepMind-style Atari. The observation is
channel-first: (c, h, w) instead of (h, w, c).
:param str env_id: the atari environment id.
:param bool episode_life: wrap the episode life wrapper.
:param bool clip_rewards: wrap the reward clipping wrapper.
:param int frame_stack: wrap the frame stacking wrapper.
:param bool scale: wrap the scaling observation wrapper.
:param bool warp_frame: wrap the grayscale + resize observation wrapper.
:return: the wrapped atari environment.
"""
assert 'MontezumaRevenge' in env_id
env = gym.make(env_id)
env = NoopResetWrapper(env, noop_max=30)
env = MaxAndSkipWrapper(env, skip=4)
if episode_life:
env = EpisodicLifeWrapper(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetWrapper(env)
if warp_frame:
env = WarpFrameWrapper(env)
if scale:
env = ScaledFloatFrameWrapper(env)
if clip_rewards:
env = ClipRewardWrapper(env)
if frame_stack:
env = FrameStackWrapper(env, frame_stack)
return env
class TimeLimit(gym.Wrapper):
def __init__(self, env, max_episode_steps=None):
super(TimeLimit, self).__init__(env)
self._max_episode_steps = max_episode_steps
self._elapsed_steps = 0
def step(self, ac):
observation, reward, done, info = self.env.step(ac)
self._elapsed_steps += 1
if self._elapsed_steps >= self._max_episode_steps:
done = True
info['TimeLimit.truncated'] = True
return observation, reward, done, info
def reset(self, **kwargs):
self._elapsed_steps = 0
return self.env.reset(**kwargs)
class WarpFrame(gym.ObservationWrapper):
def __init__(self, env, width=84, height=84, grayscale=True, dict_space_key=None):
"""
Warp frames to 84x84 as done in the Nature paper and later work.
If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which
observation should be warped.
"""
super().__init__(env)
self._width = width
self._height = height
self._grayscale = grayscale
self._key = dict_space_key
if self._grayscale:
num_colors = 1
else:
num_colors = 3
new_space = gym.spaces.Box(
low=0,
high=255,
shape=(self._height, self._width, num_colors),
dtype=np.uint8,
)
if self._key is None:
original_space = self.observation_space
self.observation_space = new_space
else:
original_space = self.observation_space.spaces[self._key]
self.observation_space.spaces[self._key] = new_space
assert original_space.dtype == np.uint8 and len(original_space.shape) == 3
def observation(self, obs):
if self._key is None:
frame = obs
else:
frame = obs[self._key]
if self._grayscale:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = cv2.resize(frame, (self._width, self._height), interpolation=cv2.INTER_AREA)
if self._grayscale:
frame = np.expand_dims(frame, -1)
if self._key is None:
obs = frame
else:
obs = obs.copy()
obs[self._key] = frame
return obs
class JpegWrapper(gym.Wrapper):
def __init__(self, env, cvt_string=True):
"""
Overview: convert the observation into string to save memory
"""
super().__init__(env)
self.cvt_string = cvt_string
def step(self, action):
observation, reward, done, info = self.env.step(action)
observation = observation.astype(np.uint8)
if self.cvt_string:
observation = jpeg_data_compressor(observation)
return observation, reward, done, info
def reset(self, **kwargs):
observation = self.env.reset(**kwargs)
observation = observation.astype(np.uint8)
if self.cvt_string:
observation = jpeg_data_compressor(observation)
return observation
class GameWrapper(gym.Wrapper):
def __init__(self, env):
"""
Overview: warp env to adapt the game interface
"""
super().__init__(env)
def legal_actions(self):
return [_ for _ in range(self.env.action_space.n)]
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